Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment

IF 4.4 2区 地球科学 Q1 GEOGRAPHY, PHYSICAL Cryosphere Pub Date : 2023-08-17 DOI:10.5194/tc-17-3329-2023
E. Alonso‐González, S. Gascoin, Sara Arioli, G. Picard
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Abstract

Abstract. The assimilation of data from Earth observation satellites into numerical models is considered to be the path forward to estimate snow cover distribution in mountain catchments, providing accurate information on the mountainous snow water equivalent (SWE). The land surface temperature (LST) can be observed from space, but its potential to improve SWE simulations remains underexplored. This is likely due to the insufficient temporal or spatial resolution offered by the current thermal infrared (TIR) missions. However, three planned missions will provide global-scale TIR data at much higher spatiotemporal resolution in the coming years. To investigate the value of TIR data to improve SWE estimation, we developed a synthetic data assimilation (DA) experiment at five snow-dominated sites covering a latitudinal gradient in the Northern Hemisphere. We generated synthetic true LST and SWE series by forcing an energy balance snowpack model with the ERA5-Land reanalysis. We used this synthetic true LST to recover the synthetic true SWE from a degraded version of ERA5-Land. We defined different observation scenarios to emulate the revisiting times of Landsat 8 (16 d) and the Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA) (3 d) while accounting for cloud cover. We replicated the experiments 100 times at each experimental site to assess the robustness of the assimilation process with respect to cloud cover under both revisiting scenarios. We performed the assimilation using two different approaches: a sequential scheme (particle filter) and a smoother (particle batch smoother). The results show that LST DA using the smoother reduced the normalized root mean square error (nRMSE) of the SWE simulations from 61 % (open loop) to 17 % and 13 % for 16 d revisit and 3 d revisit respectively in the absence of clouds. We found similar but higher nRMSE values by removing observations due to cloud cover but with a substantial increase in the standard deviation of the nRMSE of the replicates, highlighting the importance of revisiting times in the stability of the assimilation performance. The smoother largely outperformed the particle filter algorithm, suggesting that the capability of a smoother to propagate the information along the season is key to exploit LST information for snow modelling. Finally, we have compared the benefit of assimilating LST with synthetic observations of fractional snow cover area (FSCA). LST DA performed better than FSCA DA in all the study sites, suggesting that the information provided by LST is not limited to the duration of the snow season. These results suggest that the LST data assimilation has an underappreciated potential to improve snowpack simulations and highlight the value of upcoming TIR missions to advance snow hydrology.
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通过观测系统模拟实验探索热红外遥感技术改进积雪模型的潜力
摘要将对地观测卫星数据同化到数值模式中被认为是估计山区集水区积雪分布的前进路径,提供了山区雪水当量(SWE)的准确信息。陆地表面温度(LST)可以从太空观测到,但其改善SWE模拟的潜力仍未得到充分探索。这可能是由于当前热红外(TIR)任务提供的时间或空间分辨率不足。然而,三个计划中的任务将在未来几年以更高的时空分辨率提供全球尺度的TIR数据。为了探讨TIR数据对提高SWE估计的价值,我们在北半球5个积雪覆盖的站点进行了综合数据同化(DA)实验。我们利用ERA5-Land再分析强迫能量平衡积雪模型生成了合成的真实地表温度和SWE序列。我们使用这种合成真LST来从退化版本的ERA5-Land中恢复合成真SWE。我们定义了不同的观测情景,以模拟landsat 8 (16 d)和用于高分辨率自然资源评估的热红外成像卫星(TRISHNA) (3 d)的重访时间,同时考虑云层覆盖。我们在每个实验地点重复了100次实验,以评估两种重访情景下同化过程相对于云覆盖的稳健性。我们使用两种不同的方法进行同化:顺序方案(粒子过滤器)和平滑(粒子批平滑)。结果表明,在无云条件下,LST DA在16 d重访和3 d重访时,将SWE模拟的归一化均方根误差(nRMSE)分别从61%(开环)降低到17%和13%。我们发现相似的,但更高的nRMSE值通过去除观测由于云层覆盖,但在重复的nRMSE的标准偏差大幅增加,突出重访时间在同化性能的稳定性的重要性。平滑器在很大程度上优于粒子滤波算法,这表明平滑器沿季节传播信息的能力是利用地表温度信息进行雪建模的关键。最后,我们比较了同化地表温度与合成积雪面积(FSCA)观测的效益。LST数据在所有研究点的表现都优于FSCA数据,这表明LST提供的信息并不局限于雪季的持续时间。这些结果表明,地表温度数据同化在改善积雪模拟方面具有未被充分认识的潜力,并突出了即将到来的TIR任务在推进积雪水文方面的价值。
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来源期刊
Cryosphere
Cryosphere GEOGRAPHY, PHYSICAL-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
8.70
自引率
17.30%
发文量
240
审稿时长
4-8 weeks
期刊介绍: The Cryosphere (TC) is a not-for-profit international scientific journal dedicated to the publication and discussion of research articles, short communications, and review papers on all aspects of frozen water and ground on Earth and on other planetary bodies. The main subject areas are the following: ice sheets and glaciers; planetary ice bodies; permafrost and seasonally frozen ground; seasonal snow cover; sea ice; river and lake ice; remote sensing, numerical modelling, in situ and laboratory studies of the above and including studies of the interaction of the cryosphere with the rest of the climate system.
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